"Investigating leadership influence on adoption of artificial intellige" by Ernest Cornel Oduor

Date of Award

2025

Document Type

Thesis

Degree Name

Master of Arts in Digital Journalism (MADJ)

First Supervisor/Advisor

James Ndone

Second Supervisor/Advisor

Wambui Wamunyu

Department

Graduate School of Media and Communications

Abstract

This study investigated the influence of leadership on adopting Artificial Intelligence (AI) in corporate communication within two civil society organizations (CSOs)—the Kenya Human Rights Commission (KHRC) and Transparency International-Kenya (TI-Kenya). The study used a qualitative method to identify AI tools in corporate communication, examine leadership approaches that influence AI adoption, scrutinize employee perceptions of leadership influence on AI use, and identify the motivators and barriers to adopting AI. Data collected through interviews and Focus Group Discussions (FGDs) revealed that 23 AI tools were employed across various corporate communication functions to enhance efficiency and productivity. The overall finding was that, among the various leadership approaches, only autocratic and transactional styles did not influence AI adoption. The findings showed how democratic and transformational leadership styles created an environment that allowed AI use. Leadership influence over staff was either direct or indirect. The direct influence was experienced when leaders subscribed to AI tools, facilitated AI training, and used AI. Indirect influence occurred when leaders pressured their teams to deliver quality results quickly, forcing employees to turn to AI. It also happened when leaders acknowledged the quality of work produced by their team without knowing that AI aided the task. Employees who received positive feedback doubled down on AI use to keep producing good results and impress their supervisors. However, the study found that employees were divided over whether their leaders really influenced their AI adoption. One group said their leaders did not influence AI adoption, citing the generational gap. They noted that their leaders are old and not tech-savvy; as such, they cannot guide employees on a technology they know nothing about. The other group said the influence was leaders’ efforts to subscribe to AI tools, facilitate AI training, and use the technology. This study further established that employees of one of the organizations were reluctant to critique their leadership openly. This reluctance showed apprehension about expressing dissent in a hierarchical setting, a common characteristic in traditional organizational cultures. While respect for authority led to discipline and adherence to organizational rules, excessive deference stifled honest and open dialogue about innovation as staff hesitated to share feedback about leadership approaches. Key barriers to AI adoption were resource constraints, generational gaps, resistance to technology, and lack of data protection and security. Motivators such as efficiency, production of quality outputs, and social influence positively shaped employees’ willingness to adopt AI tools. The study findings align with the Technology Acceptance Model (TAM), confirming that perceived usefulness, perceived ease of use, and social factors influence technology adoption. The research concludes that leadership is critical in facilitating technological integration.

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